Currently, pattern recognition techniques have been used in various fields such as individual authentication, facial expression recognition, speech recognition, and character recognition. In general, pattern recognition is performed by extracting feature vectors from input patterns, and checking the extracted feature vector against recognition dictionary prepared in advance, thereby judging as to which category the input pattern belongs to.
Non-patent Document 1 described below proposes a pattern recognition device that calculates a distance between plural reference vectors called recognition dictionaries and input vectors representing input patterns with vectors, and on the basis of the calculation results, takes a class of reference vector having the minimum distance from the input vector as a recognition result of the input vector. At the time of learning the reference vectors, this pattern recognition device uses a gradient method to correct values of the reference vectors such that values of an evaluation function, which represents errors of learning data, decrease.
Further, Patent Document 1 described below proposes a method of learning reference vectors as described below. A first reference vector represents a reference vector having the minimum distance from the input vector of all the reference vectors belonging to the same class as the input vector. A second reference vector represents a reference vector having the minimum distance from the input vector of all reference vectors belonging to a class different from the class to which the input vector belongs. The first reference vector and the second reference vector are corrected using a value obtained through exponentiation conversion applied to a distance between an input vector and the first reference vector and a value obtained through exponentiation conversion applied to a distance between the input vector and the second reference vector.